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Principal Component Analysis (PCA) has been successfully applied to many applications, including ear recognition. This paper presents a 2D Wavelet based Multi-Band Principal Component Analysis (2D-WMBPCA) ear recognition method, inspired by PCA based techniques for multispectral and hyperspectral images. The proposed 2D-WMBPCA method performs a 2D non-decimated wavelet transform on the input image, dividing it into its wavelet subbands. Each resulting subband is then divided into a number of frames based on its coefficient&#x2019;s values. The multi frame generation boundaries are calculated using either equal size or greedy hill climbing techniques. Conventional PCA is applied on each subband&#x2019;s resulting frames, yielding its eigenvectors, which are used for matching. The intersection of the energy of the eigenvectors and the total number of features for each subband shows the number of bands which yield the highest matching performance. Experimental results on the images of two benchmark ear datasets, called IITD II and USTB I, demonstrated that the proposed 2D-WMBPCA technique significantly outperforms Single Image PCA by up to 56.79&#x0025; and the eigenfaces technique by up to 20.37&#x0025; with respect to matching accuracy. Furthermore, the proposed technique achieves very competitive results to those of learning based techniques at a fraction of their computational time and without needing to be trained.

Non-Decimated Wavelet Based Multi-Band Ear Recognition Using Principal Component Analysis